Classification of Fatigue Bills Based on K-Means by Using Creases Feature

Autor:	Nobuo Shoji, Kenji Ozawa, Masanobu Fujita, Hayao Miyagi, Dongshik Kang, Ikugo Mitsui, Satoshi Miyaguni
Rok vydání:	2010
Předmět:	Computer science Overwork Feature (machine learning) k-means clustering Classification methods Data mining computer.software_genre computer Learn vector quantization
Zdroj:	Advances in Intelligent and Soft Computing ISBN: 9783642148828 DCAI
DOI:	10.1007/978-3-642-14883-5_4
Popis:	The bills in circulation generate a large amount of fatigue bills every year, causing various types of problems, such as the paper jam in automatic tellers due to overwork and exhaustion. A highly advanced bill classification technique, which distinguishes whether a bill is a reusable bill specifying the level of fatigue, is greatly required in order to comb out these problematic bills. Therefore, a purpose of this paper is to suggest a classification method of fatigue bills based on K-means with bill image data. The effectiveness of this approach is verified by the bill discriminant experimentation.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::cb49aacade7f97ab29336c887301017a https://doi.org/10.1007/978-3-642-14883-5_4 Zobrazit plný text záznamu